基于 PCC-LSTM 模型的短期负荷预测方法

Short-term load forecasting is the basis for reasonable dispatch and smooth operation of the power grid. To improve the accuracy of short-term load forecasting, a method based on Pearson correlation coefficient (PCC) and long and short term memory (LSTM) neural network is proposed. This method uses Pearson correlation analysis to analyze the correlation of the time series composed of original multi-dimensional input variables, and selects the influencing factors with greater correlation with the power load data as the input to achieve the dimensionality reduction of the original data. Then, through the combination of LSTM neural network and Adam optimization algorithm, a network model is established for the nonlinear relationship between the influencing factors that have a greater correlation with the power load and the actual output sequence of the load. Taking the load data of Jiajie BOX and Chongqing International Grand Metropark Liyuan Hotel as calculation examples, the proposed model is compared with Prophet, LSTNet, and gated recurrent unit (GRU) models. Results show that the prediction accuracy of the proposed model is above 91%, with the highest up to 95. 44%, thus effectively improving the accuracy of load forecasting.